Top Artificial Intelligence Jobs in France

Develop and optimize large neural-network tabular models in Python. Profile training and inference pipelines, improve memory efficiency, latency, and throughput. Implement performance-critical components in Rust (and optionally C++), ensure numerical stability and reproducibility, collaborate with researchers to productionize capabilities, and maintain tests and documentation.

The Chief Data & AI Officer will strategize and implement data and AI initiatives to drive business growth and operational efficiency across Pluxee globally. Responsibilities include fostering a data-driven culture, leading data governance, ensuring compliance, and promoting the adoption of AI and advanced analytics.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Software Engineer at Polar Analytics, you will build core systems, manage data pipelines, and innovate solutions for eCommerce brands while engaging with users to improve reliability and performance.

Lead the support function by building AI agents, automation workflows, classification and knowledge systems to eliminate ticket categories. Split time between running escalations, investigating complex issues across logs and code, and hiring and developing support engineers. Own the "Support as a Product" backlog and drive product improvements from customer signal to engineering execution.

Lead AI and machine learning architecture, mentor teams, drive innovation, and evaluate tools for AI systems development across global teams.

Support design, maintenance, and operation of SCOR's AI governance framework, ensuring regulatory readiness (EU AI Act), risk management, standards, playbooks, and oversight of internal and client AI solutions. Collaborate with legal, compliance, risk, data, and technology teams to embed ethical, secure, and pragmatic AI guardrails, assess AI risks (bias, explainability, robustness), and promote governance-by-design and enablement across the organization.

Design and develop ML/DL models to integrate multi‑omics data for target identification, pathway analysis, and patient stratification. Apply transformers, representation and self‑supervised learning in HPC/cloud environments. Ensure model robustness, interpretability and reproducibility while collaborating with biologists and drug discovery teams to translate biological questions into AI solutions.

Lead enterprise deployments and enablement for Dust customers: define goals, design persona-specific training, build scalable enablement programs and content, monitor adoption, remove blockers, partner with Sales/Product, and establish repeatable best practices for AI adoption and scaling.

Lead AI-enabled transformation for insurance clients by mapping and redesigning workflows, capturing expert knowledge, co-designing AI solutions, prototyping with enterprise AI tools (LLMs, RAG, agents), and enabling scalable implementation through workshops, proposals, and handoffs to engineering teams.

Evaluate and test AI chatbots and language models in French via structured written and voice interactions. Provide ratings, write-ups, screenshots or audio recordings per task SOWs, pass language and technical assessments, and complete identity verification. Work is freelance, task-based, remote, and paid hourly.

Design, implement, and deploy AI solutions using LLMs, RAG, and AI agents. Identify AI use cases, evaluate risks and limitations, support digital and data strategy, and collaborate with teams to deliver production-ready solutions.

Lead and grow a Data & AI engineering team to design, build, operate, and optimize production data pipelines, models and platforms (AWS/Snowflake). Ensure compliance (GxP, privacy), cost optimization, automation, cross-team collaboration, and mentorship while working in an Agile product environment.

Join a 7-person AI team to design, develop, and deploy autonomous attack-investigation features using LLMs. Build agent pipelines, evaluate foundation models, create an evaluation framework, industrialize code (testing, packaging, CI/CD), and integrate and monitor solutions in production. Opportunity to work on ML/DL or data engineering projects.

As an AI Engineer, you will build AI agents for preventive health, focusing on reasoning, clinical workflows, and ensuring reliable production systems.

The Applied AI Engineer will develop automated MLOps pipelines for integrating and deploying AI models in healthcare robotics, ensuring compliance with regulations and achieving clinical accuracy targets.

Provide legal counsel on AI, privacy and data protection across customer, partner and supplier contracts; lead EU AI Act readiness and ISO 42001 alignment; advise on IP, data transfers, DPIAs, incident response, audits and regulator engagement; produce playbooks, training, and negotiation support across commercial teams.

Act as primary technical contact in pre-sales for large-scale GPU and AI projects: analyze AI workloads, design and provision custom GPU clusters, run POCs, translate constraints into business value, and collaborate with sales and engineering to close enterprise deals.

Design, scope and govern modular, secure Power Platform and Copilot solutions. Lead architecture workshops, audit environments, produce HLD/LLD, supervise implementations, manage technical teams, and define AI/Copilot standards and best practices.

Design and build business solutions and AI agents on Microsoft Power Platform and Copilot Studio. Lead workshops, audit environments, model automations, run UAT, and produce engineering and integration documentation.

Design and develop scalable .NET software architectures, integrate generative AI/ML services (Azure AI, OpenAI, ML.NET), lead technical workshops, mentor developers, enforce CI/CD/DevOps practices, conduct code reviews, and ensure software quality and security.

Design, build and industrialize Azure-based data platforms: ingest and process batch/real-time data, develop Data Lake/Warehouse/Lakehouse, implement ETL/ELT and DataOps, ensure data quality/security, collaborate with BI (Power BI), support pre-sales and produce technical documentation.

Lead and deliver change workstreams on complex transformation programmes, build multi-stakeholder adoption strategies, measure impact with KPIs and analytics, disseminate an augmented consultant model and AI agents, develop offerings and adoption kits, manage and upskill consultants, and support executive governance and client relationships.

Lead and deliver AI and data transformation engagements from strategy to implementation. Design automation and Infrastructure-as-Code solutions, mentor teams, manage change, and present to clients. Work in multidisciplinary teams on generative AI, intelligent platforms, and large-scale digital transformations.

Lead design and development of scalable .NET architectures integrating generative AI. Provide technical leadership, mentor teams, run workshops, enforce DevOps/CI/CD, code reviews, and ensure performance, quality and security across cloud-based projects (primarily Azure).